Overview

Dataset statistics

Number of variables12
Number of observations782
Missing cells314
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.4 KiB
Average record size in memory96.2 B

Variable types

Categorical3
Numeric9

Warnings

country_mapped has a high cardinality: 164 distinct values High cardinality
rank is highly correlated with score and 4 other fieldsHigh correlation
score is highly correlated with rank and 4 other fieldsHigh correlation
gdp_pc is highly correlated with rank and 3 other fieldsHigh correlation
family is highly correlated with rank and 3 other fieldsHigh correlation
health is highly correlated with rank and 3 other fieldsHigh correlation
freedom is highly correlated with rank and 1 other fieldsHigh correlation
rank is highly correlated with score and 4 other fieldsHigh correlation
score is highly correlated with rank and 4 other fieldsHigh correlation
gdp_pc is highly correlated with rank and 3 other fieldsHigh correlation
family is highly correlated with rank and 3 other fieldsHigh correlation
health is highly correlated with rank and 3 other fieldsHigh correlation
freedom is highly correlated with rank and 1 other fieldsHigh correlation
rank is highly correlated with score and 2 other fieldsHigh correlation
score is highly correlated with rank and 2 other fieldsHigh correlation
gdp_pc is highly correlated with rank and 2 other fieldsHigh correlation
health is highly correlated with rank and 2 other fieldsHigh correlation
region is highly correlated with rank and 6 other fieldsHigh correlation
rank is highly correlated with region and 6 other fieldsHigh correlation
freedom is highly correlated with region and 3 other fieldsHigh correlation
health is highly correlated with region and 4 other fieldsHigh correlation
trust is highly correlated with region and 3 other fieldsHigh correlation
dystopia is highly correlated with scoreHigh correlation
year is highly correlated with familyHigh correlation
score is highly correlated with region and 7 other fieldsHigh correlation
family is highly correlated with region and 5 other fieldsHigh correlation
gdp_pc is highly correlated with region and 4 other fieldsHigh correlation
dystopia has 312 (39.9%) missing values Missing
country_mapped is uniformly distributed Uniform
year is uniformly distributed Uniform
rank is uniformly distributed Uniform

Reproduction

Analysis started2021-08-22 19:56:18.079042
Analysis finished2021-08-22 19:56:34.240794
Duration16.16 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

country_mapped
Categorical

HIGH CARDINALITY
UNIFORM

Distinct164
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
Afghanistan
 
5
Norway
 
5
Nepal
 
5
Netherlands
 
5
New Zealand
 
5
Other values (159)
757 

Length

Max length24
Median length7
Mean length8.164961637
Min length4

Characters and Unicode

Total characters6385
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.5%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan5
 
0.6%
Norway5
 
0.6%
Nepal5
 
0.6%
Netherlands5
 
0.6%
New Zealand5
 
0.6%
Nicaragua5
 
0.6%
Niger5
 
0.6%
Nigeria5
 
0.6%
North Cyprus5
 
0.6%
Pakistan5
 
0.6%
Other values (154)732
93.6%

Length

2021-08-22T14:56:34.661222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united15
 
1.6%
republic14
 
1.5%
south14
 
1.5%
cyprus10
 
1.1%
and10
 
1.1%
congo10
 
1.1%
sudan8
 
0.9%
territories5
 
0.5%
nigeria5
 
0.5%
north5
 
0.5%
Other values (174)826
89.6%

Most occurring characters

ValueCountFrequency (%)
a1002
15.7%
i564
 
8.8%
n514
 
8.1%
e412
 
6.5%
r374
 
5.9%
o364
 
5.7%
l235
 
3.7%
t234
 
3.7%
u219
 
3.4%
s196
 
3.1%
Other values (43)2271
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5313
83.2%
Uppercase Letter912
 
14.3%
Space Separator140
 
2.2%
Open Punctuation10
 
0.2%
Close Punctuation10
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1002
18.9%
i564
10.6%
n514
9.7%
e412
 
7.8%
r374
 
7.0%
o364
 
6.9%
l235
 
4.4%
t234
 
4.4%
u219
 
4.1%
s196
 
3.7%
Other values (16)1199
22.6%
Uppercase Letter
ValueCountFrequency (%)
S102
 
11.2%
C82
 
9.0%
M74
 
8.1%
B73
 
8.0%
A63
 
6.9%
T55
 
6.0%
L48
 
5.3%
K45
 
4.9%
I45
 
4.9%
N44
 
4.8%
Other values (14)281
30.8%
Space Separator
ValueCountFrequency (%)
140
100.0%
Open Punctuation
ValueCountFrequency (%)
(10
100.0%
Close Punctuation
ValueCountFrequency (%)
)10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6225
97.5%
Common160
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1002
16.1%
i564
 
9.1%
n514
 
8.3%
e412
 
6.6%
r374
 
6.0%
o364
 
5.8%
l235
 
3.8%
t234
 
3.8%
u219
 
3.5%
s196
 
3.1%
Other values (40)2111
33.9%
Common
ValueCountFrequency (%)
140
87.5%
(10
 
6.2%
)10
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII6385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1002
15.7%
i564
 
8.8%
n514
 
8.1%
e412
 
6.5%
r374
 
5.9%
o364
 
5.7%
l235
 
3.7%
t234
 
3.7%
u219
 
3.4%
s196
 
3.1%
Other values (43)2271
35.6%

region
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)1.3%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
Sub-Saharan Africa
195 
Central and Eastern Europe
145 
Latin America and Caribbean
111 
Western Europe
105 
Middle East and Northern Africa
96 
Other values (5)
129 

Length

Max length31
Median length18
Mean length21.33930858
Min length12

Characters and Unicode

Total characters16666
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouthern Asia
2nd rowSouthern Asia
3rd rowSouthern Asia
4th rowSouthern Asia
5th rowSouthern Asia

Common Values

ValueCountFrequency (%)
Sub-Saharan Africa195
24.9%
Central and Eastern Europe145
18.5%
Latin America and Caribbean111
14.2%
Western Europe105
13.4%
Middle East and Northern Africa96
12.3%
Southeastern Asia44
 
5.6%
Southern Asia35
 
4.5%
Eastern Asia30
 
3.8%
Australia and New Zealand10
 
1.3%
North America10
 
1.3%
(Missing)1
 
0.1%

Length

2021-08-22T14:56:34.904896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-22T14:56:34.995665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
and362
15.2%
africa291
12.2%
europe250
10.5%
sub-saharan195
 
8.2%
eastern175
 
7.3%
central145
 
6.1%
america121
 
5.1%
latin111
 
4.7%
caribbean111
 
4.7%
asia109
 
4.6%
Other values (10)512
21.5%

Most occurring characters

ValueCountFrequency (%)
a2301
13.8%
r1684
 
10.1%
1601
 
9.6%
n1389
 
8.3%
e1347
 
8.1%
t871
 
5.2%
i849
 
5.1%
d564
 
3.4%
s539
 
3.2%
u534
 
3.2%
Other values (19)4987
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12655
75.9%
Uppercase Letter2215
 
13.3%
Space Separator1601
 
9.6%
Dash Punctuation195
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2301
18.2%
r1684
13.3%
n1389
11.0%
e1347
10.6%
t871
 
6.9%
i849
 
6.7%
d564
 
4.5%
s539
 
4.3%
u534
 
4.2%
o435
 
3.4%
Other values (8)2142
16.9%
Uppercase Letter
ValueCountFrequency (%)
A531
24.0%
E521
23.5%
S469
21.2%
C256
11.6%
N116
 
5.2%
L111
 
5.0%
W105
 
4.7%
M96
 
4.3%
Z10
 
0.5%
Space Separator
ValueCountFrequency (%)
1601
100.0%
Dash Punctuation
ValueCountFrequency (%)
-195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14870
89.2%
Common1796
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2301
15.5%
r1684
 
11.3%
n1389
 
9.3%
e1347
 
9.1%
t871
 
5.9%
i849
 
5.7%
d564
 
3.8%
s539
 
3.6%
u534
 
3.6%
A531
 
3.6%
Other values (17)4261
28.7%
Common
ValueCountFrequency (%)
1601
89.1%
-195
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII16666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2301
13.8%
r1684
 
10.1%
1601
 
9.6%
n1389
 
8.3%
e1347
 
8.1%
t871
 
5.2%
i849
 
5.1%
d564
 
3.4%
s539
 
3.2%
u534
 
3.2%
Other values (19)4987
29.9%

year
Categorical

HIGH CORRELATION
UNIFORM

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2015
158 
2016
157 
2018
156 
2019
156 
2017
155 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3128
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2016
3rd row2017
4th row2018
5th row2019

Common Values

ValueCountFrequency (%)
2015158
20.2%
2016157
20.1%
2018156
19.9%
2019156
19.9%
2017155
19.8%

Length

2021-08-22T14:56:35.278448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-22T14:56:35.355339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2015158
20.2%
2016157
20.1%
2018156
19.9%
2019156
19.9%
2017155
19.8%

Most occurring characters

ValueCountFrequency (%)
2782
25.0%
0782
25.0%
1782
25.0%
5158
 
5.1%
6157
 
5.0%
8156
 
5.0%
9156
 
5.0%
7155
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3128
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2782
25.0%
0782
25.0%
1782
25.0%
5158
 
5.1%
6157
 
5.0%
8156
 
5.0%
9156
 
5.0%
7155
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common3128
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2782
25.0%
0782
25.0%
1782
25.0%
5158
 
5.1%
6157
 
5.0%
8156
 
5.0%
9156
 
5.0%
7155
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2782
25.0%
0782
25.0%
1782
25.0%
5158
 
5.1%
6157
 
5.0%
8156
 
5.0%
9156
 
5.0%
7155
 
5.0%

rank
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct158
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.69820972
Minimum1
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2021-08-22T14:56:35.458508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.05
Q140
median79
Q3118
95-th percentile149
Maximum158
Range157
Interquartile range (IQR)78

Descriptive statistics

Standard deviation45.18238438
Coefficient of variation (CV)0.5741221375
Kurtosis-1.199701126
Mean78.69820972
Median Absolute Deviation (MAD)39
Skewness0.0004973514565
Sum61542
Variance2041.447859
MonotonicityNot monotonic
2021-08-22T14:56:35.581226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
346
 
0.8%
1456
 
0.8%
826
 
0.8%
576
 
0.8%
1535
 
0.6%
235
 
0.6%
145
 
0.6%
745
 
0.6%
775
 
0.6%
755
 
0.6%
Other values (148)728
93.1%
ValueCountFrequency (%)
15
0.6%
25
0.6%
35
0.6%
45
0.6%
55
0.6%
65
0.6%
75
0.6%
85
0.6%
95
0.6%
105
0.6%
ValueCountFrequency (%)
1581
 
0.1%
1572
 
0.3%
1564
0.5%
1555
0.6%
1545
0.6%
1535
0.6%
1525
0.6%
1515
0.6%
1505
0.6%
1495
0.6%

score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct716
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.379017903
Minimum2.693000078
Maximum7.769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2021-08-22T14:56:35.711691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.693000078
5-th percentile3.58715
Q14.50975
median5.322
Q36.1895
95-th percentile7.31395
Maximum7.769
Range5.075999922
Interquartile range (IQR)1.67975

Descriptive statistics

Standard deviation1.12745646
Coefficient of variation (CV)0.2096026599
Kurtosis-0.7610545866
Mean5.379017903
Median Absolute Deviation (MAD)0.846
Skewness0.03585943327
Sum4206.392
Variance1.27115807
MonotonicityNot monotonic
2021-08-22T14:56:35.845598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3793
 
0.4%
5.8353
 
0.4%
5.893
 
0.4%
5.1293
 
0.4%
2.9053
 
0.4%
4.353
 
0.4%
5.1923
 
0.4%
6.3753
 
0.4%
4.6812
 
0.3%
5.7432
 
0.3%
Other values (706)754
96.4%
ValueCountFrequency (%)
2.6930000781
 
0.1%
2.8391
 
0.1%
2.8531
 
0.1%
2.9049999711
 
0.1%
2.9053
0.4%
3.0061
 
0.1%
3.0691
 
0.1%
3.0832
0.3%
3.2031
 
0.1%
3.2311
 
0.1%
ValueCountFrequency (%)
7.7691
0.1%
7.6321
0.1%
7.61
0.1%
7.5941
0.1%
7.5871
0.1%
7.5611
0.1%
7.5551
0.1%
7.5541
0.1%
7.5370001791
0.1%
7.5271
0.1%

gdp_pc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct742
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9160474825
Minimum0
Maximum2.096
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2021-08-22T14:56:35.985677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.208797
Q10.6065
median0.9822047088
Q31.236187109
95-th percentile1.487882078
Maximum2.096
Range2.096
Interquartile range (IQR)0.629687109

Descriptive statistics

Standard deviation0.4073401313
Coefficient of variation (CV)0.4446714161
Kurtosis-0.6927595054
Mean0.9160474825
Median Absolute Deviation (MAD)0.2998890486
Skewness-0.3185805094
Sum716.3491313
Variance0.1659259826
MonotonicityNot monotonic
2021-08-22T14:56:36.113265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
0.6%
0.964
 
0.5%
1.343
 
0.4%
0.3323
 
0.4%
1.0432
 
0.3%
0.6422
 
0.3%
1.3012
 
0.3%
0.6892
 
0.3%
1.2212
 
0.3%
1.2632
 
0.3%
Other values (732)755
96.5%
ValueCountFrequency (%)
05
0.6%
0.01531
 
0.1%
0.016041
 
0.1%
0.022643184291
 
0.1%
0.0241
 
0.1%
0.0261
 
0.1%
0.0461
 
0.1%
0.056611
 
0.1%
0.068311
 
0.1%
0.0691
 
0.1%
ValueCountFrequency (%)
2.0961
0.1%
1.8707656861
0.1%
1.824271
0.1%
1.7419435981
0.1%
1.697521
0.1%
1.692277671
0.1%
1.690421
0.1%
1.6841
0.1%
1.6491
0.1%
1.645551
0.1%

family
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct732
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.078392483
Minimum0
Maximum1.644
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2021-08-22T14:56:36.231219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.46133
Q10.8693625
median1.124735
Q31.32725
95-th percentile1.522
Maximum1.644
Range1.644
Interquartile range (IQR)0.4578875

Descriptive statistics

Standard deviation0.3295483193
Coefficient of variation (CV)0.305592189
Kurtosis0.1584486833
Mean1.078392483
Median Absolute Deviation (MAD)0.235555
Skewness-0.6846322898
Sum843.3029213
Variance0.1086020948
MonotonicityNot monotonic
2021-08-22T14:56:36.351683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
0.6%
1.5383
 
0.4%
1.4383
 
0.4%
1.4653
 
0.4%
1.1253
 
0.4%
1.413
 
0.4%
1.5043
 
0.4%
1.3192
 
0.3%
1.2232
 
0.3%
1.4592
 
0.3%
Other values (722)753
96.3%
ValueCountFrequency (%)
05
0.6%
0.104191
 
0.1%
0.110371
 
0.1%
0.139951
 
0.1%
0.1471
 
0.1%
0.148661
 
0.1%
0.185191
 
0.1%
0.192491
 
0.1%
0.234421
 
0.1%
0.247491
 
0.1%
ValueCountFrequency (%)
1.6441
0.1%
1.6241
0.1%
1.6105740071
0.1%
1.6011
0.1%
1.5921
0.1%
1.591
0.1%
1.5871
0.1%
1.5841
0.1%
1.5831
0.1%
1.5822
0.3%

health
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct705
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6124155771
Minimum0
Maximum1.141
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2021-08-22T14:56:36.479736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1578945
Q10.4401825
median0.6473095147
Q30.808
95-th percentile0.954973
Maximum1.141
Range1.141
Interquartile range (IQR)0.3678175

Descriptive statistics

Standard deviation0.2483086404
Coefficient of variation (CV)0.4054577474
Kurtosis-0.487571207
Mean0.6124155771
Median Absolute Deviation (MAD)0.1686446949
Skewness-0.5012025622
Sum478.9089813
Variance0.06165718089
MonotonicityNot monotonic
2021-08-22T14:56:36.621398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
0.6%
0.9995
 
0.6%
0.8155
 
0.6%
0.8284
 
0.5%
0.8744
 
0.5%
0.8843
 
0.4%
0.9143
 
0.4%
0.8543
 
0.4%
0.8713
 
0.4%
0.8613
 
0.4%
Other values (695)744
95.1%
ValueCountFrequency (%)
05
0.6%
0.0055647538971
 
0.1%
0.011
 
0.1%
0.01877268591
 
0.1%
0.038241
 
0.1%
0.041134715081
 
0.1%
0.044761
 
0.1%
0.047761
 
0.1%
0.0481
 
0.1%
0.048642169681
 
0.1%
ValueCountFrequency (%)
1.1411
0.1%
1.1221
0.1%
1.0881
0.1%
1.0621
0.1%
1.0521
0.1%
1.0451
0.1%
1.0422
0.3%
1.0392
0.3%
1.0362
0.3%
1.031
0.1%

freedom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct697
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4110908258
Minimum0
Maximum0.724
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2021-08-22T14:56:36.750072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.128096
Q10.3097675
median0.431
Q30.531
95-th percentile0.6308885
Maximum0.724
Range0.724
Interquartile range (IQR)0.2212325

Descriptive statistics

Standard deviation0.1528804206
Coefficient of variation (CV)0.3718896434
Kurtosis-0.3072054061
Mean0.4110908258
Median Absolute Deviation (MAD)0.10948
Skewness-0.5212591254
Sum321.4730258
Variance0.02337242301
MonotonicityNot monotonic
2021-08-22T14:56:36.889973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
0.6%
0.5574
 
0.5%
0.4173
 
0.4%
0.3123
 
0.4%
0.3563
 
0.4%
0.5163
 
0.4%
0.4063
 
0.4%
0.5313
 
0.4%
0.5083
 
0.4%
0.4313
 
0.4%
Other values (687)749
95.8%
ValueCountFrequency (%)
05
0.6%
0.005891
 
0.1%
0.011
 
0.1%
0.0131
 
0.1%
0.014995855281
 
0.1%
0.0161
 
0.1%
0.0251
 
0.1%
0.0261
 
0.1%
0.030369857331
 
0.1%
0.04321
 
0.1%
ValueCountFrequency (%)
0.7241
0.1%
0.6961
0.1%
0.6861
0.1%
0.6831
0.1%
0.6811
0.1%
0.6771
0.1%
0.6741
0.1%
0.669731
0.1%
0.6691
0.1%
0.665571
0.1%

trust
Real number (ℝ≥0)

HIGH CORRELATION

Distinct635
Distinct (%)81.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.1254356136
Minimum0
Maximum0.55191
Zeros6
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2021-08-22T14:56:37.078902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.018
Q10.054
median0.091
Q30.15603
95-th percentile0.37124
Maximum0.55191
Range0.55191
Interquartile range (IQR)0.10203

Descriptive statistics

Standard deviation0.1058164476
Coefficient of variation (CV)0.8435917404
Kurtosis1.880108294
Mean0.1254356136
Median Absolute Deviation (MAD)0.04745
Skewness1.5208882
Sum97.9652142
Variance0.01119712057
MonotonicityNot monotonic
2021-08-22T14:56:37.212843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0827
 
0.9%
0.0286
 
0.8%
0.0646
 
0.8%
0.0786
 
0.8%
06
 
0.8%
0.0346
 
0.8%
0.0565
 
0.6%
0.0745
 
0.6%
0.0555
 
0.6%
0.0935
 
0.6%
Other values (625)724
92.6%
ValueCountFrequency (%)
06
0.8%
0.0011
 
0.1%
0.002271
 
0.1%
0.003221
 
0.1%
0.0041
 
0.1%
0.0043879006991
 
0.1%
0.0051
 
0.1%
0.0063
0.4%
0.006151
 
0.1%
0.006491
 
0.1%
ValueCountFrequency (%)
0.551911
0.1%
0.522081
0.1%
0.505211
0.1%
0.49211
0.1%
0.483571
0.1%
0.480491
0.1%
0.469871
0.1%
0.4643077851
0.1%
0.4571
0.1%
0.45522001391
0.1%

generosity
Real number (ℝ≥0)

Distinct664
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2185758416
Minimum0
Maximum0.838075161
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2021-08-22T14:56:37.352903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05403037467
Q10.13
median0.2019822115
Q30.2788325
95-th percentile0.4704543383
Maximum0.838075161
Range0.838075161
Interquartile range (IQR)0.1488325

Descriptive statistics

Standard deviation0.1223207487
Coefficient of variation (CV)0.5596261135
Kurtosis2.020258278
Mean0.2185758416
Median Absolute Deviation (MAD)0.07322149014
Skewness1.044360015
Sum170.9263081
Variance0.01496236557
MonotonicityNot monotonic
2021-08-22T14:56:37.483089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1756
 
0.8%
0.1535
 
0.6%
0.1875
 
0.6%
05
 
0.6%
0.0994
 
0.5%
0.0834
 
0.5%
0.1974
 
0.5%
0.1423
 
0.4%
0.1483
 
0.4%
0.1853
 
0.4%
Other values (654)740
94.6%
ValueCountFrequency (%)
05
0.6%
0.001991
 
0.1%
0.010164656681
 
0.1%
0.020251
 
0.1%
0.0251
 
0.1%
0.0262
 
0.3%
0.026411
 
0.1%
0.027361
 
0.1%
0.0288068411
 
0.1%
0.0291
 
0.1%
ValueCountFrequency (%)
0.8380751611
0.1%
0.819711
0.1%
0.795881
0.1%
0.61170458791
0.1%
0.5981
0.1%
0.586961
0.1%
0.57631
0.1%
0.57473057511
0.1%
0.57212311031
0.1%
0.5661
0.1%

dystopia
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct470
Distinct (%)100.0%
Missing312
Missing (%)39.9%
Infinite0
Infinite (%)0.0%
Mean2.092716638
Minimum0.32858
Maximum3.83772
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2021-08-22T14:56:37.617391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.32858
5-th percentile1.126492703
Q11.737975
median2.09464
Q32.455574545
95-th percentile3.025559
Maximum3.83772
Range3.50914
Interquartile range (IQR)0.7175995455

Descriptive statistics

Standard deviation0.5657717565
Coefficient of variation (CV)0.2703527779
Kurtosis0.4141306299
Mean2.092716638
Median Absolute Deviation (MAD)0.3595132296
Skewness-0.1216469469
Sum983.5768199
Variance0.3200976805
MonotonicityNot monotonic
2021-08-22T14:56:37.753407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.95211
 
0.1%
1.598881
 
0.1%
1.8788902761
 
0.1%
2.403641
 
0.1%
2.043841
 
0.1%
2.792489291
 
0.1%
3.182861
 
0.1%
3.107091
 
0.1%
2.474891
 
0.1%
2.2770266531
 
0.1%
Other values (460)460
58.8%
(Missing)312
39.9%
ValueCountFrequency (%)
0.328581
0.1%
0.37791371351
0.1%
0.41938924791
0.1%
0.54006123541
0.1%
0.55463314061
0.1%
0.62113046651
0.1%
0.654291
0.1%
0.670421
0.1%
0.671081
0.1%
0.81438231471
0.1%
ValueCountFrequency (%)
3.837721
0.1%
3.602141
0.1%
3.559061
0.1%
3.507331
0.1%
3.409041
0.1%
3.380071
0.1%
3.351681
0.1%
3.310291
0.1%
3.260011
0.1%
3.221341
0.1%

Interactions

2021-08-22T14:56:23.817831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:24.011600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:24.141847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:24.256434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:24.372823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:24.486335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:24.611891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:24.736945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:24.856061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:24.973286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:25.100565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:25.205277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:25.303679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:25.408539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:25.509340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:25.619538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:25.866222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:25.988533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:26.092957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:26.198211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:26.294817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:26.391638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:26.499351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:26.618895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:26.754721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:26.877405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:26.974985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:27.095729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:27.257482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:27.382344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:27.492315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:27.605287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:27.711432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:27.824617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:27.953051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:28.084256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:28.213711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:28.327950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:28.429966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:28.528953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:28.656101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:28.777565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:28.901003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:29.015393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:29.120483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:29.224867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:29.371246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:29.500195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:29.613893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:29.844406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:29.961759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:30.086632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:30.210759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:30.323771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:30.430930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:30.538753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:30.643462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:30.751004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:30.855508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:30.960708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:31.069798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:31.171540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:31.270750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:31.377375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:31.482923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:31.581154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:31.679130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:31.784873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:31.896284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:32.031250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:32.146586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:32.256836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:32.372360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:32.500232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:32.618678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:32.744108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:32.871018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:32.982200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:33.093865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:33.195186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T14:56:33.305020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-08-22T14:56:38.128262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-22T14:56:38.286088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-22T14:56:38.451148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-22T14:56:38.635340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-22T14:56:38.788117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-22T14:56:33.561187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-22T14:56:33.807939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-22T14:56:34.012486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-22T14:56:34.128638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

country_mappedregionyearrankscoregdp_pcfamilyhealthfreedomtrustgenerositydystopia
0AfghanistanSouthern Asia20151533.5750.3198200.3028500.3033500.2341400.0971900.3651001.952100
1AfghanistanSouthern Asia20161543.3600.3822700.1103700.1734400.1643000.0711200.3126802.145580
2AfghanistanSouthern Asia20171413.7940.4014770.5815430.1807470.1061800.0611580.3118712.150801
3AfghanistanSouthern Asia20181453.6320.3320000.5370000.2550000.0850000.0360000.191000NaN
4AfghanistanSouthern Asia20191543.2030.3500000.5170000.3610000.0000000.0250000.158000NaN
5AlbaniaCentral and Eastern Europe2015954.9590.8786700.8043400.8132500.3573300.0641300.1427201.898940
6AlbaniaCentral and Eastern Europe20161094.6550.9553000.5016300.7300700.3186600.0530100.1684001.928160
7AlbaniaCentral and Eastern Europe20171094.6440.9961930.8036850.7311600.3814990.0398640.2013131.490442
8AlbaniaCentral and Eastern Europe20181124.5860.9160000.8170000.7900000.4190000.0320000.149000NaN
9AlbaniaCentral and Eastern Europe20191074.7190.9470000.8480000.8740000.3830000.0270000.178000NaN

Last rows

country_mappedregionyearrankscoregdp_pcfamilyhealthfreedomtrustgenerositydystopia
772ZambiaSub-Saharan Africa2015855.1290.4703800.9161200.2992400.4882700.1246800.1959102.634300
773ZambiaSub-Saharan Africa20161064.7950.6120200.6376000.2357300.4266200.1147900.1786602.589910
774ZambiaSub-Saharan Africa20171164.5140.6364071.0031870.2578360.4616030.0782140.2495801.826705
775ZambiaSub-Saharan Africa20181254.3770.5620001.0470000.2950000.5030000.0820000.221000NaN
776ZambiaSub-Saharan Africa20191384.1070.5780001.0580000.4260000.4310000.0870000.247000NaN
777ZimbabweSub-Saharan Africa20151154.6100.2710001.0327600.3347500.2586100.0807900.1898702.441910
778ZimbabweSub-Saharan Africa20161314.1930.3504100.7147800.1595000.2542900.0858200.1850302.442700
779ZimbabweSub-Saharan Africa20171383.8750.3758471.0830960.1967640.3363840.0953750.1891431.597970
780ZimbabweSub-Saharan Africa20181443.6920.3570001.0940000.2480000.4060000.0990000.132000NaN
781ZimbabweSub-Saharan Africa20191463.6630.3660001.1140000.4330000.3610000.0890000.151000NaN